Recently, contrastive learning [1]}, [2]} has achieved great progress
in unsupervised learning problem. SimCLR [2]} proposes a simple framework to perform contrastive learning, where positive pairs are generated with two random augmented views of the same image and negative ones are obtained with different images, forming an image-level discrimination task. Furthermore, MoCo [1]} maintains a queue of negative samples and turns one branch of Siamese network into a momentum encoder to improve consistency of the queue. Recently [5]} has extended unsupervised contrastive learning to fully-supervised setting that can effectively leverage label information. This setting has been used in semantic segmentation to improve the state-of-the-art performance. [6]}, [7]} contrast the pixel embedding between different semantic categories in a supervised manner to aid in segmentation.
